Core Implementation of SVM-based Text Classification

This document presents the core implementation of Support Vector Machine (SVM) based text classification. The code includes essential components such as feature vectorization, kernel matrix computation, and quadratic optimization solving using sequential minimal optimization (SMO) algorithm. The implementation supports both linear and non-linear kernel functions (RBF, polynomial) with configurable hyperparameters for model tuning. This codebase is intended for research collaboration and academic exploration to advance machine learning methodologies in text classification domains. The architecture employs efficient memory management for large-scale text datasets and includes cross-validation utilities for performance evaluation. Commercial use is strictly prohibited. We encourage researchers to utilize this implementation for experimenting with SVM variations, feature engineering techniques, and classification benchmarks. For technical inquiries regarding kernel selection, parameter optimization, or integration with text preprocessing pipelines, please contact our research team. We welcome collaborative opportunities to further develop and refine text classification methodologies.